book

Privacy and Security for Large Language Models

Name: Privacy and Security for Large Language Models
Author: Baihan Lin
ISBN: 9781098160845

by Baihan Lin

January 2026

Intermediate to advanced

318 pages

8h 44m

English

O'Reilly Media, Inc.

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Includes

Quizzes

Preface
Who Should Read This BookWhy I Wrote This BookNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. Introduction
The Rise of Large Language ModelsPrivacy and Security Concerns in LLMsWhat This Book CoversYour Role in This JourneySummary
2. Understanding Large Language Models
Fundamentals of Large Language ModelsBasic Building Blocks of Language ModelsKey Concepts in LLMsLLM ArchitecturesTransformer ArchitectureMixture of Experts ArchitecturePopular LLM ModelsTraining Techniques for LLMsPre-Training TechniquesFine-Tuning TechniquesRetrieval-Augmented GenerationSummary
3. Evaluating the Privacy and Security Risks of LLMs
Privacy MetricsDifferential PrivacyPrivacy Lossk-anonymityPrivacy Considerations in RAG SystemsSecurity MetricsAttack Success Rate (ASR)False Positive Rate (FPR) for Membership InferenceReconstruction Error for Model InversionLLM Privacy and Security AuditsSimulating AttacksLLMPrivacySecurityEvaluator: The All-in-One AuditorModern Evaluation Frameworks and BenchmarksSummary
4. Privacy-Preserving Training Techniques
A Real-World Example of Privacy Breach in the Training PhaseSynthetic Data for Privacy EvaluationHow to Apply LLMPrivacySecurityEvaluator on Your DataDifferential Privacy for LLMsThe Mathematical FoundationImplementing DP-SGD for LLMsPrivacy Accounting in PracticeTrade-Offs and ConsiderationsApplying Differential Privacy to Retrieval-Augmented GenerationFederated Learning with LLMsThe ConceptImplementing Federated Learning for LLMsAdvantages and Challenges of Federated LearningHomomorphic Encryption in LLMsThe ConceptImplementing HE for LLMsAdvantages and Challenges of Homomorphic EncryptionMulti-Party Computation for Secure AggregationThe ConceptImplementing MPC with Modern LibrariesAdvantages and Challenges of MPCParameter-Efficient Fine-Tuning for PrivacyLow-Rank AdaptationQuantized Low-Rank AdaptationPrivacy-Preserving Data TransformationData Anonymization and De-IdentificationPrivacy-Preserving Data AugmentationAdvantages and Challenges of Privacy-Preserving Data AugmentationSummary
5. Secure Deployment of LLMs
Secure Model Hosting and InfrastructureUnderstanding Infrastructure ComponentsIsolation StrategiesNetwork SecurityResource Management and MonitoringSecure APIs and CommunicationsAPI Design PrinciplesImplementation of Secure APIsAuthentication and AuthorizationSecure CommunicationSecure Model Versioning and UpdatesModel Registry and Version ControlSecure Update ProcessSummary
6. Adversarial Attacks and Defenses
Understanding Adversarial Attacks on LLMsTaxonomy of Adversarial Attacks on LLMsNotable Attack MethodsEmbedding Space AttacksLLM Agent AttacksImpact of Model Scale and ArchitectureCase Study: Defending Against Jailbreaking AttacksRobust Fine-Tuning TechniquesAdversarial TrainingRobust Optimization TechniquesData Augmentation for RobustnessPrefix-Tuning and Prompt-Based RobustnessEnsemble MethodsCertifiably Robust Fine-TuningRed-Teaming LLMsRed-Teaming MethodologiesImplementing a Red-Teaming ProgramRed-Teaming Tools and FrameworksAutomated Multiround Red-TeamingCase Study: Red-Teaming in PracticeAdversarial Evaluation and Robustness MetricsRobustness BenchmarksRobustness Under Distribution ShiftHuman-in-the-Loop EvaluationAgent-Based EvaluationStandardized Attack Success MetricsDefense Evaluation MetricsChallenges in Robustness EvaluationBest PracticesFuture Directions in LLM RobustnessSummary
7. Ethical Considerations in Fine-Tuning LLMs
Bias and Fairness Issues in PersonalizationUnderstanding Bias in Fine-Tuned LLMsMeasuring Fairness in Fine-Tuned ModelsBias Mitigation StrategiesChallenges in Privacy-Preserving Bias MitigationTransparency and Explainability in Fine-Tuned ModelsThe Explainability Challenge in LLMsTechniques for Explaining LLM BehaviorPrivacy-Preserving ExplainabilityAddressing AI Bias with Privacy ConstraintsThe Privacy-Fairness Trade-OffGroup-Aware Privacy MechanismsBias-Aware Federated LearningPrivacy-Preserving Bias AuditingSummary
8. Navigating the Cultural, Social, and Legal Landscapes
A New Kind of Socio-Technical SystemsRiding Amidst an AI-Mediated Cultural EvolutionThe Rise of AI-Generated Content and the Erosion of TrustPersonalized AI and Identity Crisis in the Age of Surveillance CapitalismExistential Questions in Human-Machine InteractionUnveiling the Generative AI Supply ChainThe Emergence of Machine CultureAdaptable Legal Frameworks for Regulation and AccountabilityThe Case of Copyright and Intellectual Property in the Age of LLMsThe Case of Data Privacy and Protection in Personalized AI SystemsThe Case of Algorithmic Bias and Discrimination in AI-Powered Decision MakingThe Case of Liability and Accountability in AI-Powered SystemsUniversal Challenges to Techno-Legal SolutionismBuilding a Responsible AI CultureAI Safety Beyond Algorithms: The Human ElementsSummary
9. Building Privacy-Preserving AI Capabilities
Healthcare AI in Action: Differentially Private Clinical Note AnalysisThe Healthcare Privacy ChallengeSynthetic Data as a Privacy-Preserving FoundationLoRA: Efficient and Privacy-Friendly Fine-TuningPrivacy Accounting with RDPReal-World Deployment ConsiderationsLegal AI in Action: Federated Learning Across Law Firms or CourtsThe Legal Confidentiality ImperativeFederated Learning Architecture for Legal AISecure Aggregation and Model UpdatesLegal and Ethical Considerations in Federated Legal AIPerformance and Utility EvaluationBuilding Your Privacy-First AI CapabilityOrganizational Readiness and Implementation StrategyTeam Structure and Technology DecisionsGovernance Integration and Success MeasurementPreparing for Tomorrow’s Privacy LandscapeTechnology Convergence and Regulatory EvolutionMarket Dynamics and Competitive PositioningA Strategic Position for the FutureSummaryConclusionThe Transformation You’ve WitnessedThe Path We’re OnYour Role in Shaping the Future

Index
About the Author

Content preview from Privacy and Security for Large Language Models

About the Author

Dr. Baihan Lin is a leading computer scientist, neuroscientist, inventor, and professor specializing in speech and natural language processing (NLP). He holds research and faculty positions at the Berkman Klein Center for Internet and Society at Harvard University and the Icahn School of Medicine at Mount Sinai. Known for his expertise in trustworthy NeuroAI and computational psychiatry, Dr. Lin has made significant contributions to these fields through his work at Columbia University, where he earned his PhD, and through his research at leading tech companies such as IBM, Google, Microsoft, Amazon, and BGI Genomics.

His research program focuses on developing intelligent speech and text-based systems to enhance human-AI and human-human interactions in healthcare with a focus on privacy and security. Notably, he developed the first-ever online and reinforcement learning (RL)–based speaker diarization system and interactive spoken language understanding (SLU) systems for children with speech and communication disorders.

Dr. Lin’s work in deep learning and NLP has led to real-world applications deployed in high-stakes environments, such as AI companions for therapists, conversational agents in primary care practice, and context-aware virtual realities for rehabilitation. He has authored over 100 peer-reviewed publications and patents, and has served on program committees and editorial boards for over 40 top conferences and journals. He has chaired tutorials, workshops, ...

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Privacy and Security for Large Language Models

by Baihan Lin

About the Author

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.